import pandas as pd
import statsmodels.formula.api as smf
from sklearn.model_selection import train_test_split
from sqlalchemy import create_engine
from sklearn.metrics import r2_score,mean_absolute_error
from matplotlib import pyplot as plt
import numpy as np
import seaborn as sns
import pymysql

db_config = {
    'host':'127.0.0.1',
    'user':'root',
    'password':'root',
    'database':'tushare1',
    'port':3306,
    'charset':'utf8'
}

engine = create_engine(
    f"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?{db_config['charset']}"
)
conn = pymysql.connect(**db_config)

chunk_size = 10000

df = pd.read_sql_query(
    """
    SELECT d.*,m.buy_lg_vol,m.sell_lg_vol,m.buy_elg_vol,m.sell_elg_vol,m.net_mf_vol,i.vol as i_vol,i.closes as i_closes
    FROM date_1 d JOIN moneyflows m on d.ts_code = m.ts_code and d.trade_date = m.trade_date
    LEFT JOIN index_daily i on i.trade_date = d.trade_date and i.ts_code = '399001.SZ'
    WHERE d.trade_date BETWEEN '2023-01-01' and '2024-01-01' and d.ts_code = '002229.SZ'
    """,
    conn,
    chunksize=chunk_size
)

df1 = pd.concat(df,ignore_index=True)

df1['zd_closes'] = round(((df1['closes'] -df1['closes'].shift(1)) / df1['closes'].shift(1)),2)
df1['zs_closes'] = round(((df1['i_closes'] -df1['i_closes'].shift(1)) / df1['i_closes'].shift(1)),2)
df1['zs_vol'] = round(((df1['i_vol'] -df1['i_vol'].shift(1)) / df1['i_vol'].shift(1)),2)

df1 = df1.dropna(subset=['zd_closes','zs_closes','zs_vol'])
print(df1.head)

ex = ["id","ts_code","trade_date","the_date","opens","high","low","closes","pre_closes","changes","pct_chg","amount"]
number = df1.select_dtypes(include=['number']).columns.to_list()
newList = [col for col in number if col not in ex]

formuls = 'zd_closes ~ ' + ' + '.join(newList)
res = smf.ols(formuls,data=df1).fit()

print(res.summary())

plt.figure(figsize=(10,6))
plt.scatter(res.fittedvalues,df1['zd_closes'])
plt.show()

features = df1[['vol','buy_lg_vol','sell_lg_vol','buy_elg_vol','sell_elg_vol','net_mf_vol','i_vol','i_closes','zs_closes','zs_vol']]
correlatuin_matrix = features.corr()

plt.figure(figsize=(10,6))
sns.heatmap(correlatuin_matrix,annot=True,cmap='coolwarm',cbar=True,fmt='.2f',linewidths=.5)
plt.show()